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Major Release v0.6

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@MaartenGr MaartenGr released this 09 Mar 13:23
· 168 commits to master since this release
1ffc456

Highlights:

  • DTM: Added a basic dynamic topic modeling technique based on the global c-TF-IDF representation
    • model.topics_over_time(docs, timestamps, global_tuning=True)
  • DTM: Option to evolve topics based on t-1 c-TF-IDF representation which results in evolving topics over time
    • Only uses topics at t-1 and skips evolution if there is a gap
    • model.topics_over_time(docs, timestamps, evolution_tuning=True)
  • DTM: Function to visualize topics over time
    • model.visualize_topics_over_time(topics_over_time)
  • DTM: Add binning of timestamps
    • model.topics_over_time(docs, timestamps, nr_bins=10)
  • Add function get general information about topics (id, frequency, name, etc.)
    • get_topic_info()
  • Improved stability of c-TF-IDF by taking the average number of words across all topics instead of the number of documents

Fixes:

  • _map_probabilities() does not take into account that there is no probability of the outlier class and the probabilities are mutated instead of copied (#63, #64)